5 Best Ways to Find Common Elements in Multiple Python Dicts

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💡 Problem Formulation: Python developers often need to identify common keys or key-value pairs across multiple dictionaries. This can arise when comparing configurations, datasets, or consolidating information. For example, given two dictionaries dict_a = {'x': 1, 'y': 2} and dict_b = {'y': 2, 'z': 3}, the goal is to find that the common element is {'y': 2}.

Method 1: Intersection of Keys with Dictionary Comprehension

This method leverages dictionary comprehension and the intersection of keys to extract common elements from multiple dictionaries. More specifically, it gathers keys present in all dictionaries and then constructs a new dictionary only with those keys and their corresponding values.

Here’s an example:

dict_a = {'x': 1, 'y': 2, 'z': 4}
dict_b = {'y': 2, 'z': 3, 'w': 8}
dict_c = {'y': 2, 'z': 4, 'u': 1}

common_keys = set(dict_a).intersection(dict_b, dict_c)
common_elements = {k: dict_a[k] for k in common_keys if all(dict_a[k] == d.get(k) for d in [dict_b, dict_c])}

print(common_elements)

Output:

{'y': 2}

This code snippet first converts the keys of dict_a into a set and then uses the intersection method to find common keys across all dictionaries. A new dictionary is then built with dictionary comprehension by iterating over the common keys and checking for equality of values in all dictionaries.

Method 2: Using functools.reduce and operator.and_

The functools module’s reduce function and the operator module’s and_ operation can be applied to perform a cumulative intersection of dictionary key sets. This approach is more functional and can elegantly scale for multiple dictionaries.

Here’s an example:

from functools import reduce
import operator

dict_a = {'x': 1, 'y': 2, 'z': 4}
dict_b = {'y': 2, 'z': 3, 'w': 8}
dict_c = {'y': 2, 'z': 4, 'u': 1}

common_keys = reduce(operator.and_, (set(d) for d in [dict_a, dict_b, dict_c]))
common_elements = {k: dict_a[k] for k in common_keys if all(dict_a[k] == d.get(k) for d in [dict_b, dict_c])}

print(common_elements)

Output:

{'y': 2}

This code utilizes reduce to apply the intersection operator and_ across the sets of keys from each dictionary. After obtaining the common keys, a new dictionary with common elements is created, ensuring the value matching is consistent across dictionaries.

Method 3: ChainMap for Overlapping Keys

Using collections.ChainMap, we can gather all dictionaries into one context and then screen for overlapping keys. This method is particularly useful when values are not relevant, or we only need the keys.

Here’s an example:

from collections import ChainMap

dict_a = {'x': 1, 'y': 2}
dict_b = {'y': 3, 'z': 4}
dict_c = {'y': 2, 'x': 1}

chain = ChainMap(dict_a, dict_b, dict_c)
common_keys = {key for key in chain if list(chain.values()).count(chain[key]) == len(chain.maps)}

print(common_keys)

Output:

{'x', 'y'}

This snippet uses ChainMap to unify the dictionaries and then applies a set comprehension to find keys whose values appear as many times as there are dictionaries, suggesting that the key is common across all.

Method 4: Filter and Lambdas

This functional approach employs filtering and lambda functions to identify common key-value pairs. It filters key-value pairs from one dictionary based on their presence in all other dictionaries.

Here’s an example:

dict_a = {'x': 1, 'y': 2}
dict_b = {'y': 2, 'z': 3}
dict_c = {'y': 2, 'x': 1, 'z': 4}

common_elements = dict(filter(lambda item: all(item in d.items() for d in [dict_b, dict_c]), dict_a.items()))

print(common_elements)

Output:

{'y': 2}

The code filters dict_a.items(), yielding a new dictionary where each item is present in the other dictionaries. The lambda function verifies the presence of each item across each dictionaries’ items using a generator expression within the all function.

Bonus One-Liner Method 5: Use Set Operations in a Comprehension

For a quick one-liner resolution, we can use set operations to find common keys in dictionaries within a dictionary comprehension, assuming the values are not considered.

Here’s an example:

dict_a = {'x': 1, 'y': 2}
dict_b = {'y': 2, 'z': 3}
dict_c = {'y': 2, 'x': 10}

common_keys = set(dict_a) & set(dict_b) & set(dict_c)

print(common_keys)

Output:

{'y'}

By converting the dictionaries’ keys to sets and chaining the intersection operator, we quickly arrive at a set of keys present in all dictionaries. Note that values aren’t compared here; it’s purely key-based.

Summary/Discussion

  • Method 1: Intersection of Keys with Dictionary Comprehension. Strengths: Transparent and Pythonic. Weaknesses: Can become verbose with multiple dictionaries.
  • Method 2: Using functools.reduce and operator.and_. Strengths: Compact and scalable for multiple dictionaries. Weaknesses: Requires understanding of functional programming concepts.
  • Method 3: ChainMap for Overlapping Keys. Strengths: Straightforward for extracting keys. Weaknesses: Might not be suitable if value comparison is necessary.
  • Method 4: Filter and Lambdas. Strengths: Functional and concise. Weaknesses: Can be less readable for those not familiar with lambdas.
  • Method 5: One-Liner Set Operation. Strengths: Extremely concise. Weaknesses: Limited to keys and can be less readable.